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Vegetation Regeneration and Disturbance Estimates through Time, forest change detection algorithm. This algorithm generates a yearly clear-sky composite from satellite imagery, calculates a spectral vegetation index for each pixel in that composite, spatially segments the vegetation index image into patches, temporally divides the time series into differently sloped segments, and then labels those segments as disturbed, stable, or regenerating. Segmentation at both the spatial and temporal steps are performed using total variation regularization.
The output consists of a 1D array per pixel containing the slope of fitted trend lines. Negative values indicate disturbance and positive values regeneration.
See: Hughes, M.J., Kaylor, S.D. and Hayes, D.J., 2017. Patch-based forest change detection from Landsat time series. Forests, 8(5), p.166.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-10-06 UTC."],[[["\u003cp\u003eThe algorithm detects forest change by analyzing yearly satellite imagery and identifying areas of disturbance and regeneration.\u003c/p\u003e\n"],["\u003cp\u003eIt uses a spectral vegetation index and segments the imagery spatially and temporally to track vegetation changes.\u003c/p\u003e\n"],["\u003cp\u003eDisturbance is indicated by negative trend slopes, while regeneration is indicated by positive slopes in the output array.\u003c/p\u003e\n"],["\u003cp\u003eThe algorithm utilizes total variation regularization for both spatial and temporal segmentation to enhance accuracy.\u003c/p\u003e\n"],["\u003cp\u003eUsers can adjust parameters like tolerance, regularization, and maximum runs for customized analysis using the provided function.\u003c/p\u003e\n"]]],[],null,["# ee.Algorithms.TemporalSegmentation.Verdet\n\nVegetation Regeneration and Disturbance Estimates through Time, forest change detection algorithm. This algorithm generates a yearly clear-sky composite from satellite imagery, calculates a spectral vegetation index for each pixel in that composite, spatially segments the vegetation index image into patches, temporally divides the time series into differently sloped segments, and then labels those segments as disturbed, stable, or regenerating. Segmentation at both the spatial and temporal steps are performed using total variation regularization.\n\n\u003cbr /\u003e\n\nThe output consists of a 1D array per pixel containing the slope of fitted trend lines. Negative values indicate disturbance and positive values regeneration.\n\nSee: Hughes, M.J., Kaylor, S.D. and Hayes, D.J., 2017. Patch-based forest change detection from Landsat time series. Forests, 8(5), p.166.\n\n| Usage | Returns |\n|------------------------------------------------------------------------------------------------|---------|\n| `ee.Algorithms.TemporalSegmentation.Verdet(timeSeries, `*tolerance* `, `*alpha* `, `*nRuns*`)` | Image |\n\n| Argument | Type | Details |\n|--------------|-------------------------------------|----------------------------------------------------------------------------------------------------------------------------------|\n| `timeSeries` | ImageCollection | Collection from which to extract VeRDET scores. This collection is expected to contain 1 image for each year, sorted temporally. |\n| `tolerance` | Float, default: 0.0001 | Convergence tolerance. |\n| `alpha` | Float, default: 0.03333333333333333 | Regularization parameter for segmentation. |\n| `nRuns` | Integer, default: 100 | Maximum number of runs for convergence. |"]]
